A probe data service has been defined as a means for vehicular on-board units (OBUs) to transmit the data collected by their in-vehicle sensors in real time via wireless communication links to road-site units (RSUs). Sensor data fusion algorithms are developed in this work to aggregate data collected through this probe data service for computing single-RSU and multi-RSU average per-division speeds and average route travel times, where a division is defined to be a small segment of a road. These algorithms are evaluated using an integrated simulation testbed that consists of a vehicular simulator and a wireless communication link simulator, which are interconnected via a TCP/IP connection. Our key findings are that the probe data service offers an excellent means for computing average per-division speeds and average route travel times when the market penetration level of OBUs is relatively high. Privacy constraints negatively impact the accuracy of route travel time estimates as routes often span roads under the wireless coverage area of many road-side units requiring frequent vehicle identity changes. The impact of wireless communication losses is quite significant, requiring the use of accurate models in any simulation-based studies.

Wireless Access in Vehicular Environments (WAVE) networks operating in the Dedicated Short Range Communications (DSRC) band have been deployed as testbeds on which trials have been conducted recently. The goal of this paper is to analyze field-test measurements from these trials, and to use the data to validate assumptions made in simulation models. First, we analyzed the field-test measurements for message success rate and characterized its dependence on distance. Next, using trial-and-error methods, we found the “best” channel model assumptions that fit this field-test data. It consists of using the Nakagami model for fast fading, free space and shadowing model for slow fading, and a shadowing correlation model for retransmissions and acknowledgments. As the field tests were conducted in a variety of environments (tall and short urban-canyon as well as open field), this best-fit model is a generic model that will account for the variability that can be expected in actual deployments as vehicles move between different environments.